Paper Number
1257
Paper Type
Short Paper
Abstract
This research in progress delves into the highly relevant phenomenon of worker resistance against algorithmic management within the gig economy, particularly in the food-delivery sector. Despite the growing academic interest in the phenomenon of resistance, previous studies have neglected the multi-level bottom-up process in which resistance develops and escalates from individual workers to the worker collective. We therefore propose a computational multi-level longitudinal approach to investigate the evolution and escalation trajectory of worker resistance, leveraging a large language machine learning model and digital trace data from 3 million Reddit posts from 2016 to 2022. We provide insights into our approach, discuss challenges, and display initial results.
Recommended Citation
Weber, Matthias; de Jong, Alexander Willem; and Remus, Ulrich, "Towards a Multi-Level Model of Resistance — A Computational Trace-Data Approach" (2024). ECIS 2024 Proceedings. 5.
https://aisel.aisnet.org/ecis2024/track05_fow/track05_fow/5
Towards a Multi-Level Model of Resistance — A Computational Trace-Data Approach
This research in progress delves into the highly relevant phenomenon of worker resistance against algorithmic management within the gig economy, particularly in the food-delivery sector. Despite the growing academic interest in the phenomenon of resistance, previous studies have neglected the multi-level bottom-up process in which resistance develops and escalates from individual workers to the worker collective. We therefore propose a computational multi-level longitudinal approach to investigate the evolution and escalation trajectory of worker resistance, leveraging a large language machine learning model and digital trace data from 3 million Reddit posts from 2016 to 2022. We provide insights into our approach, discuss challenges, and display initial results.
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